Bulk RNA-seq

Note

More comprehensive examples are provided for these data types:

• scRNA-seq

• Flow cytometry

# !pip install 'lamindb[jupyter,bionty]'
!lamin init --storage test-bulkrna --schema bionty

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💡 connected lamindb: testuser1/test-bulkrna

import lamindb as ln
from pathlib import Path
import bionty as bt
import pandas as pd
import anndata as ad

💡 connected lamindb: testuser1/test-bulkrna

Ingest data

Access

We start by simulating a nf-core RNA-seq run which yields us a count matrix artifact.

(See Nextflow for running this with Nextflow.)

# pretend we're running a bulk RNA-seq pipeline
ln.track(transform=ln.Transform(name="nf-core RNA-seq", reference="https://nf-co.re/rnaseq"))
# create a directory for its output
Path("./test-bulkrna/output_dir").mkdir(exist_ok=True)
# get the count matrix
path = ln.core.datasets.file_tsv_rnaseq_nfcore_salmon_merged_gene_counts(
    populate_registries=True
)
# move it into the output directory
path = path.rename(f"./test-bulkrna/output_dir/{path.name}")
# register it
ln.Artifact(path, description="Merged Bulk RNA counts").save()

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💡 saved: Transform(uid='mW5MekmWlxYoUl4Q', name='nf-core RNA-seq', type='pipeline', reference='https://nf-co.re/rnaseq', created_by_id=1, updated_at='2024-08-06 18:33:02 UTC')

💡 saved: Run(uid='SLo51hIsaNN9KWOWpWhC', transform_id=1, created_by_id=1)

Artifact(uid='0LjedQgK7Lo6qRVsY6Yx', description='Merged Bulk RNA counts', key='output_dir/salmon.merged.gene_counts.tsv', suffix='.tsv', size=3787, hash='xxw0k3au3KtxFcgtbEr4eQ', _hash_type='md5', visibility=1, _key_is_virtual=False, created_by_id=1, storage_id=1, transform_id=1, run_id=1, updated_at='2024-08-06 18:33:04 UTC')

Transform

ln.settings.transform.stem_uid = "s5V0dNMVwL9i"
ln.settings.transform.version = "0"
ln.track()

💡 notebook imports: anndata==0.10.8 bionty==0.47.1 lamindb==0.75.0 pandas==1.5.3

💡 saved: Transform(uid='s5V0dNMVwL9i6K79', version='0', name='Bulk RNA-seq', key='bulkrna', type='notebook', created_by_id=1, updated_at='2024-08-06 18:33:04 UTC')

💡 saved: Run(uid='KGRMbsfm3JLeo3xx0ly7', transform_id=2, created_by_id=1)

Run(uid='KGRMbsfm3JLeo3xx0ly7', started_at='2024-08-06 18:33:04 UTC', is_consecutive=True, transform_id=2, created_by_id=1)

Let’s query the artifact:

artifact = ln.Artifact.filter(description="Merged Bulk RNA counts").one()

df = artifact.load()

If we look at it, we realize it deviates far from the tidy data standard Wickham14, conventions of statistics & machine learning Hastie09, Murphy12 and the major Python & R data packages.

Variables are not in columns and observations are not in rows:

df

	gene_id	gene_name	RAP1_IAA_30M_REP1	RAP1_UNINDUCED_REP1	RAP1_UNINDUCED_REP2	WT_REP1	WT_REP2
0	Gfp_transgene_gene	Gfp_transgene_gene	0.0	0.000	0.0	0.0	0.0
1	HRA1	HRA1	0.0	8.572	0.0	0.0	0.0
2	snR18	snR18	3.0	8.000	4.0	8.0	8.0
3	tA(UGC)A	TGA1	0.0	0.000	0.0	0.0	0.0
4	tL(CAA)A	SUP56	0.0	0.000	0.0	0.0	0.0
...	...	...	...	...	...	...	...
120	YAR064W	YAR064W	0.0	2.000	0.0	0.0	0.0
121	YAR066W	YAR066W	3.0	13.000	8.0	5.0	11.0
122	YAR068W	YAR068W	9.0	28.000	24.0	5.0	7.0
123	YAR069C	YAR069C	0.0	0.000	0.0	0.0	1.0
124	YAR070C	YAR070C	0.0	0.000	0.0	0.0	0.0

125 rows × 7 columns

Let’s change that and move observations into rows:

df = df.T

df

	0	1	2	3	4	5	6	7	8	9	...	115	116	117	118	119	120	121	122	123	124
gene_id	Gfp_transgene_gene	HRA1	snR18	tA(UGC)A	tL(CAA)A	tP(UGG)A	tS(AGA)A	YAL001C	YAL002W	YAL003W	...	YAR050W	YAR053W	YAR060C	YAR061W	YAR062W	YAR064W	YAR066W	YAR068W	YAR069C	YAR070C
gene_name	Gfp_transgene_gene	HRA1	snR18	TGA1	SUP56	TRN1	tS(AGA)A	TFC3	VPS8	EFB1	...	FLO1	YAR053W	YAR060C	YAR061W	YAR062W	YAR064W	YAR066W	YAR068W	YAR069C	YAR070C
RAP1_IAA_30M_REP1	0.0	0.0	3.0	0.0	0.0	0.0	1.0	55.0	36.0	632.0	...	4.357	0.0	1.0	0.0	1.0	0.0	3.0	9.0	0.0	0.0
RAP1_UNINDUCED_REP1	0.0	8.572	8.0	0.0	0.0	0.0	0.0	72.0	33.0	810.0	...	15.72	0.0	0.0	0.0	3.0	2.0	13.0	28.0	0.0	0.0
RAP1_UNINDUCED_REP2	0.0	0.0	4.0	0.0	0.0	0.0	0.0	115.0	82.0	1693.0	...	13.772	0.0	4.0	0.0	2.0	0.0	8.0	24.0	0.0	0.0
WT_REP1	0.0	0.0	8.0	0.0	0.0	1.0	0.0	60.0	63.0	1115.0	...	13.465	0.0	0.0	0.0	1.0	0.0	5.0	5.0	0.0	0.0
WT_REP2	0.0	0.0	8.0	0.0	0.0	0.0	0.0	30.0	25.0	704.0	...	6.891	0.0	1.0	0.0	0.0	0.0	11.0	7.0	1.0	0.0

7 rows × 125 columns

Now, it’s clear that the first two rows are in fact no observations, but descriptions of the variables (or features) themselves.

Let’s create an AnnData object to model this. First, create a dataframe for the variables:

var = pd.DataFrame({"gene_name": df.loc["gene_name"].values}, index=df.loc["gene_id"])

var.head()

	gene_name
gene_id
Gfp_transgene_gene	Gfp_transgene_gene
HRA1	HRA1
snR18	snR18
tA(UGC)A	TGA1
tL(CAA)A	SUP56

Now, let’s create an AnnData:

# we're also fixing the datatype here, which was string in the tsv
adata = ad.AnnData(df.iloc[2:].astype("float32"), var=var)

adata

AnnData object with n_obs × n_vars = 5 × 125
    var: 'gene_name'

The AnnData object is in tidy form and complies with conventions of statistics and machine learning:

adata.to_df()

gene_id	Gfp_transgene_gene	HRA1	snR18	tA(UGC)A	tL(CAA)A	tP(UGG)A	tS(AGA)A	YAL001C	YAL002W	YAL003W	...	YAR050W	YAR053W	YAR060C	YAR061W	YAR062W	YAR064W	YAR066W	YAR068W	YAR069C	YAR070C
RAP1_IAA_30M_REP1	0.0	0.000	3.0	0.0	0.0	0.0	1.0	55.0	36.0	632.0	...	4.357	0.0	1.0	0.0	1.0	0.0	3.0	9.0	0.0	0.0
RAP1_UNINDUCED_REP1	0.0	8.572	8.0	0.0	0.0	0.0	0.0	72.0	33.0	810.0	...	15.720	0.0	0.0	0.0	3.0	2.0	13.0	28.0	0.0	0.0
RAP1_UNINDUCED_REP2	0.0	0.000	4.0	0.0	0.0	0.0	0.0	115.0	82.0	1693.0	...	13.772	0.0	4.0	0.0	2.0	0.0	8.0	24.0	0.0	0.0
WT_REP1	0.0	0.000	8.0	0.0	0.0	1.0	0.0	60.0	63.0	1115.0	...	13.465	0.0	0.0	0.0	1.0	0.0	5.0	5.0	0.0	0.0
WT_REP2	0.0	0.000	8.0	0.0	0.0	0.0	0.0	30.0	25.0	704.0	...	6.891	0.0	1.0	0.0	0.0	0.0	11.0	7.0	1.0	0.0

5 rows × 125 columns

Validate

Let’s create a Artifact object from this AnnData.

Almost all gene IDs are validated:

genes = bt.Gene.from_values(
    adata.var.index,
    bt.Gene.stable_id,
    organism="saccharomyces cerevisiae",  # or set globally with bt.settings.organism
)

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❗ did not create Gene records for 2 non-validated stable_ids: 'Gfp_transgene_gene', 'YAR062W'

# also register the 2 non-validated genes obtained from Bionty
ln.save(genes)

Register

efs = bt.ExperimentalFactor.lookup()
organism = bt.Organism.lookup()
features = ln.Feature.lookup()

curated_file = ln.Artifact.from_anndata(
    adata,
    description="Curated bulk RNA counts"
)

Hence, let’s save this artifact:

curated_file.save()

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Artifact(uid='L2Y7GInXNYcIVbpA0Mx9', description='Curated bulk RNA counts', suffix='.h5ad', type='dataset', _accessor='AnnData', size=28180, hash='6bieh8XjOCCz6bJToN4u1g', _hash_type='md5', visibility=1, _key_is_virtual=True, created_by_id=1, storage_id=1, transform_id=2, run_id=2, updated_at='2024-08-06 18:33:06 UTC')

Link to validated metadata records:

curated_file.features._add_set_from_anndata(var_field=bt.Gene.stable_id, organism="saccharomyces cerevisiae")

❗    2 terms (1.60%) are not validated for stable_id: Gfp_transgene_gene, YAR062W

curated_file.labels.add(efs.rna_seq, features.assay)
curated_file.labels.add(organism.saccharomyces_cerevisiae, features.organism)

curated_file.describe()

Artifact(uid='L2Y7GInXNYcIVbpA0Mx9', description='Curated bulk RNA counts', suffix='.h5ad', type='dataset', _accessor='AnnData', size=28180, hash='6bieh8XjOCCz6bJToN4u1g', _hash_type='md5', visibility=1, _key_is_virtual=True, updated_at='2024-08-06 18:33:06 UTC')
  Provenance
    .created_by = 'testuser1'
    .storage = '/home/runner/work/lamin-usecases/lamin-usecases/docs/test-bulkrna'
    .transform = 'Bulk RNA-seq'
    .run = '2024-08-06 18:33:04 UTC'
  Labels
    .organisms = 'saccharomyces cerevisiae'
    .experimental_factors = 'RNA-Seq'
  Features
    'assay' = 'RNA-Seq'
    'organism' = 'saccharomyces cerevisiae'
  Feature sets
    'var' = 'None', 'TGA1', 'SUP56', 'TRN1', 'TFC3', 'VPS8', 'EFB1', 'SSA1', 'ERP2', 'FUN14', 'SPO7', 'MDM10', 'SWC3', 'CYS3', 'DEP1', 'SYN8', 'NTG1'

Query data

We have two files in the artifact registry:

ln.Artifact.df()

	uid	version	description	key	suffix	type	_accessor	size	hash	_hash_type	n_objects	n_observations	visibility	_key_is_virtual	storage_id	transform_id	run_id	created_by_id	updated_at
id
2	L2Y7GInXNYcIVbpA0Mx9	None	Curated bulk RNA counts	None	.h5ad	dataset	AnnData	28180	6bieh8XjOCCz6bJToN4u1g	md5	None	None	1	True	1	2	2	1	2024-08-06 18:33:06.398477+00:00
1	0LjedQgK7Lo6qRVsY6Yx	None	Merged Bulk RNA counts	output_dir/salmon.merged.gene_counts.tsv	.tsv	None	None	3787	xxw0k3au3KtxFcgtbEr4eQ	md5	None	None	1	False	1	1	1	1	2024-08-06 18:33:04.397164+00:00

curated_file.view_lineage()

Let’s by query by gene:

genes = bt.Gene.lookup()

genes.spo7

Gene(uid='2pkcLeMEB6aS', symbol='SPO7', stable_id='YAL009W', ncbi_gene_ids='851224', biotype='protein_coding', synonyms='', description='Putative regulatory subunit of Nem1p-Spo7p phosphatase holoenzyme; regulates nuclear growth by controlling phospholipid biosynthesis, required for normal nuclear envelope morphology, premeiotic replication, and sporulation ', created_by_id=1, run_id=2, organism_id=1, source_id=19, updated_at='2024-08-06 18:33:06 UTC')

# a gene set containing SPO7
feature_set = ln.FeatureSet.filter(genes=genes.spo7).first()

# artifacts that link to this feature set
ln.Artifact.filter(feature_sets=feature_set).df()

	uid	version	description	key	suffix	type	_accessor	size	hash	_hash_type	n_objects	n_observations	visibility	_key_is_virtual	storage_id	transform_id	run_id	created_by_id	updated_at
id
2	L2Y7GInXNYcIVbpA0Mx9	None	Curated bulk RNA counts	None	.h5ad	dataset	AnnData	28180	6bieh8XjOCCz6bJToN4u1g	md5	None	None	1	True	1	2	2	1	2024-08-06 18:33:06.398477+00:00

# clean up test instance
!lamin delete --force test-bulkrna
!rm -r test-bulkrna

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Traceback (most recent call last):
  File "/opt/hostedtoolcache/Python/3.10.14/x64/bin/lamin", line 8, in <module>
    sys.exit(main())
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/rich_click/rich_command.py", line 367, in __call__
    return super().__call__(*args, **kwargs)
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/click/core.py", line 1157, in __call__
    return self.main(*args, **kwargs)
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/rich_click/rich_command.py", line 152, in main
    rv = self.invoke(ctx)
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/click/core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/click/core.py", line 1434, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/click/core.py", line 783, in invoke
    return __callback(*args, **kwargs)
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/lamin_cli/__main__.py", line 164, in delete
    return delete(instance, force=force)
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/lamindb_setup/_delete.py", line 98, in delete
    n_objects = check_storage_is_empty(
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/lamindb_setup/core/upath.py", line 779, in check_storage_is_empty
    raise InstanceNotEmpty(message)
lamindb_setup.core.upath.InstanceNotEmpty: Storage /home/runner/work/lamin-usecases/lamin-usecases/docs/test-bulkrna/.lamindb contains 1 objects ('_is_initialized' ignored) - delete them prior to deleting the instance